What Is OpenSearch?

OpenSearch is an open source search and analytics suite for distributed search, logging, and data analysis at scale. Developed and maintained by the OpenSearch community, it was created as a fork from Elasticsearch 7.10.2 after licensing changes by Elastic. 

OpenSearch provides a scalable, RESTful search engine capable of full-text search, log aggregation, and analytics, suitable for various data streaming and log management needs. It is written in Java and provides a platform for constructing custom search and analytics solutions.

OpenSearch includes features such as automatic sharding, document-level security, machine learning, alerting, and index lifecycle management. Its open licensing under the Apache 2.0 License makes it a popular choice for organizations seeking a free and open source alternative for their search and analytics infrastructure. 

The project emphasizes extensibility and security, allowing developers to build upon its core and integrate it with existing systems and workflows without licensing concerns.

What Is Kibana?

Kibana is an open source analytics and visualization platform for data stored in Elasticsearch. Developed by Elastic, Kibana allows users to explore, visualize, and interact with data indexed in Elasticsearch through dashboards and visualizations. Its primary use cases include log and event data analysis, time-series analytics, and geospatial data exploration.

Kibana provides a suite of visualization tools, including charts, tables, and maps, with support for real-time data interaction. The platform enables users to build dynamic dashboards, run ad hoc queries, and generate reports for data-driven insights. 

Kibana integrates closely with the Elastic stack, benefitting from a mature ecosystem and commercial features, but its licensing has shifted from open source to Elastic’s Server Side Public License (SSPL), which introduces restrictions for some deployment scenarios.

In this article:

  • Why Was the OpenSearch Project Forked from Elasticsearch and Kibana?
  • Similarities Between OpenSearch Dashboards and Kibana
  • OpenSearch Dashboards vs. Kibana: Key Differences
  • OpenSearch vs. Kibana: How to Choose?

Why Was the OpenSearch Project Forked from Elasticsearch and Kibana?

The OpenSearch project was initiated by Amazon Web Services in 2021 as a response to changes in the licensing model of Elasticsearch and Kibana. With version 7.11, Elastic transitioned its codebase from the Apache 2.0 License to SSPL and Elastic License, limiting the freedom to use, modify, and distribute these tools in certain environments, particularly in commercial and cloud-based applications. This shift posed challenges for organizations relying on fully open source licensing for their infrastructure, prompting the need for an alternative.

By forking Elasticsearch and Kibana at version 7.10.2, OpenSearch ensured that the search and analytics community could continue to innovate with open source solutions under transparent licensing. The OpenSearch project inherited much of the original functionality but adopted a community-driven development model. This move preserved open access to features, plugins, and extensibility while removing restrictions associated with Elastic’s newer licenses.

Similarities Between OpenSearch Dashboards and Kibana

OpenSearch Dashboards and Kibana share a common origin, and as a result, their user interfaces, workflow, and core functionalities are very similar, especially through the versions immediately after the fork. Both platforms function as visualization and analytics front-ends, allowing users to connect to search backends (OpenSearch or Elasticsearch) and create interactive dashboards, charts, and data visualizations. 

Index pattern management, query builders, and tools for time-series and log analytics present a familiar experience to users switching between the two. Both tools enable exploratory data analysis with similar navigational menus, visualization options, and dashboard-building experiences. 

Early releases of OpenSearch Dashboards aimed for feature parity with Kibana, minimizing friction for users migrating or managing hybrid environments. This alignment in user experience and workflows ensures that organizations can transition between platforms with minimal re-training or workflow adjustment in the initial adoption period.

Tips from the expert

Kassian Wren

Kassian Wren

Open Source Technology Evangelist

Kassian Wren is an Open Source Technology Evangelist specializing in OpenSearch. They are known for their expertise in developing and promoting open-source technologies, and have contributed significantly to the OpenSearch community through talks, events, and educational content

In my experience, here are tips that can help you better evaluate, migrate, and operate between OpenSearch Dashboards and Kibana:

  1. Assess Your Licensing and Long-Term Strategy: OpenSearch Dashboards, with its Apache 2.0 License, offers freedom from vendor lock-in. In contrast, Kibana’s SSPL and Elastic License have more restrictions, especially for commercial use. Your choice should align with your long-term goals and need for flexibility.
  2. Map Your Required Features to What’s Free: Identify the critical features you need and check their availability. OpenSearch Dashboards offers features like security, alerting, and anomaly detection for free, while Kibana includes many in paid tiers. A feature checklist helps compare costs and ensures you get the tools you need without surprises.
  3. Confirm Backend Compatibility for a Smooth Operation: Your visualization tool must align with its search backend. The OpenSearch Dashboard is designed for OpenSearch, while Kibana is built for Elasticsearch. Versions beyond 7.10.2 have diverged, making cross-compatibility risky and unsupported. Stick to native pairings—OpenSearch Dashboards with OpenSearch and Kibana with Elasticsearch—to ensure stability, support, and access to the latest features.
  4. Plan for Operational Excellence and Scalability: Think about how you’ll manage, secure, and scale your platform. Both OpenSearch Dashboards and Kibana need expertise for efficient production use. For OpenSearch users, a managed service can handle deployment, monitoring, security, and scaling. This lets your team focus on analyzing data instead of managing infrastructure, ensuring a powerful, resilient, and secure environment from the start.

OpenSearch Dashboards vs. Kibana: Key Differences

1. Licensing and Project Origin

OpenSearch Dashboards operates under the Apache 2.0 License, meaning it is free and open source with no commercial usage restrictions. Kibana, since version 7.11, is licensed under SSPL and the Elastic License, which restricts certain types of use, especially for service providers and commercial entities. OpenSearch Dashboards emerged from a community-led fork intended to preserve open source principles, while Kibana remains closely tied to Elastic’s commercial objectives and licensing controls.

The diverging project origins impact how each project approaches governance, contribution, and future direction. OpenSearch, backed by AWS and an open community, invites features and enhancements from a broad contributor base under a transparent process. Elastic controls Kibana’s development, prioritizing features that fit its commercial ecosystem and requiring users to navigate Elastic’s licensing complexities.

2. Features and Plugins

OpenSearch Dashboards and Kibana offer overlapping core feature sets, such as dashboards, data visualizations, and management interfaces. However, their plugin ecosystems and bundled features have started to diverge. OpenSearch Dashboards prioritizes security, alerting, and observability plugins out-of-the-box, which are part of its open source distribution. Features like anomaly detection, reporting, and alerting are included as standard.

Kibana segments many advanced capabilities, such as security analytics or reporting, into commercial tiers, accessible only on paid Elastic subscriptions. Its plugin system is robust but may include features gated behind proprietary licenses. As both platforms evolve, distinctions in the scope and accessibility of plugins are likely to increase, with OpenSearch focusing on expanded open source access and Kibana deepening integration within Elastic’s stack.

3. Community and Support

OpenSearch is governed by an open community model, inviting contributions from users, organizations, and vendors. AWS provides significant backing but development is transparent, with public roadmaps and open governance. Community support is supplemented by detailed documentation, forums, and growing third-party integrations.

Kibana primarily benefits from the centralized direction of Elastic. While it has an active community and comprehensive documentation, the project’s evolution is determined by Elastic’s business priorities and customer feedback from its user base. Enterprise users have access to Elastic support contracts, but community influence on roadmap decisions is more limited compared to the open governance seen in the OpenSearch project.

4. Compatibility and Usage Context

OpenSearch Dashboards is tightly integrated with OpenSearch clusters and maintains compatibility with data formats and APIs up to the point of the fork. Subsequent releases have introduced new features and, in some cases, incompatibilities with newer versions of Elasticsearch. This makes OpenSearch Dashboards the obvious choice for users running OpenSearch clusters or those prioritizing open source alignment.

Kibana pairs exclusively with Elasticsearch, evolving rapidly alongside it. New developments in Elasticsearch, such as data stream improvements, machine learning, or custom connectors, are typically accessible only through the corresponding Kibana releases. Organizations committed to the Elastic ecosystem, or requiring the latest feature set, may prefer Kibana.

5. Long-Term Evolution

The roadmap for OpenSearch Dashboards emphasizes continued open development, frequent feature additions, and community-led innovation. New releases often focus on stability, expanded analytics capabilities, and improved integrations, with an emphasis on keeping the platform accessible under the same permissive license. The pace and direction are influenced by community priorities, industry requirements, and contributions from major stakeholders like AWS.

Kibana’s evolution is closely linked to Elastic’s commercial strategy. Future enhancements are likely to improve integration with Elastic Cloud, AI-powered features, and value-added capabilities available in premium subscriptions. The direction prioritizes differentiation through proprietary features and ecosystem synergy, potentially restricting some innovations to customers within the paid and licensed environment.

OpenSearch vs. Kibana: How to Choose?

Choosing between OpenSearch Dashboards and Kibana depends on your technical requirements, licensing constraints, and ecosystem alignment. While they share a common ancestry, their divergence in governance, features, and support models makes each better suited for different scenarios.

Key considerations for choosing between OpenSearch and Kibana:

  • Licensing requirements: If your organization mandates a fully open source stack, OpenSearch Dashboards is the better fit due to its Apache 2.0 License. Kibana’s SSPL and Elastic License impose restrictions, especially for commercial and cloud service providers.
  • Feature accessibility: OpenSearch Dashboards bundles advanced features like security, alerting, and anomaly detection at no cost. Kibana provides many of these only through paid tiers, so consider your budget and need for enterprise features.
  • Search backend alignment: Use OpenSearch Dashboards with OpenSearch clusters and Kibana with Elasticsearch clusters. Cross-compatibility beyond version 7.10.2 is limited and unsupported.
  • Customization and extensibility: OpenSearch supports a broader plugin framework under permissive licensing, which is suitable for custom workflows or community-driven improvements. Kibana plugins may be limited by proprietary code or licensing terms.
  • Governance and community model: Choose OpenSearch if open governance and community-driven development are important to your organization. Kibana’s direction is controlled by Elastic, aligning with their commercial roadmap.
  • Deployment environment: For cloud-native or self-hosted environments requiring freedom to distribute or customize, OpenSearch is typically more flexible. Kibana is optimized for Elastic Cloud and may be restricted in other deployment contexts.
  • Innovation and roadmap stability: Kibana may adopt newer features faster due to Elastic’s dedicated R&D, but OpenSearch offers predictability and transparency in its development cycle, which can be critical for regulated or large-scale systems.

 

Comparing Management Options: Instaclustr for OpenSearch versus Elasticsearch

Choosing the right search and analytics engine is a critical decision for businesses. It impacts everything from application performance to total cost of ownership. While Elasticsearch was once the default choice, licensing changes have created uncertainty and risk. On the other hand, OpenSearch offers a truly open source alternative.  Pairing OpenSearch with the NetApp Instaclustr Managed Platform provides a powerful, scalable, and future-proof solution.

The Open Source Advantage: OpenSearch vs. Elasticsearch

The core difference between OpenSearch and Elasticsearch boils down to one fundamental principle: the freedom of open source. This isn’t just a philosophical debate; it has real-world consequences for budgets, innovation, and operational flexibility.

Freedom from Vendor Lock-In

Vendor lock-in is a serious business risk. When infrastructure is built on proprietary technology, it becomes dependent on a single company for pricing, support, and future development. Elasticsearch’s licensing strategy steers users toward its paid cloud services, creating a walled garden.

Instaclustr’s commitment to 100% open source technology frees users from this trap. By choosing Instaclustr for OpenSearch, users retain complete ownership of data and the technology stack. If an organization decides to move, they can take the open source solution along without facing prohibitive licensing fees or technical barriers. Instaclustr enables organizations to focus on building amazing applications, not navigating restrictive vendor agreements.

Unlock Peak Performance with Instaclustr for OpenSearch

Running a distributed system like OpenSearch at scale is no small task. It requires deep expertise in deployment, monitoring, security, and optimization. Instaclustr transforms a powerful open source tool into a reliable, enterprise-grade service.

Data workloads are not static. Users need a system that can grow, handling traffic spikes and expanding datasets without a hitch. Instaclustr empowers organizations to scale OpenSearch clusters horizontally with just a few clicks, adding or removing nodes as needs change.

Instaclustr handles the complex backend orchestration, ensuring clusters remain balanced, performant, and highly available. Instaclustr experts have provisioned and managed some of the largest OpenSearch deployments in the world, bringing experience and best practices for reliability and resilience.

Enterprise-Grade Security, Built-In

Instaclustr includes a suite of robust security features enabled by default. This includes encryption in-transit and at-rest, comprehensive access controls, and detailed audit logging. The Instaclustr team ensures clusters are hardened and compliant with industry standards, protecting sensitive data from unauthorized access. This gives users peace of mind knowing search and analytics engines are secured by open source experts.

Your Path to a Better Search Solution

Instaclustr provides the tools, expertise, and support to ensure OpenSearch environments are powerful and secure enabling organizations to deliver innovation and value to their customers. For more information: